Predicting the risk of subclinical atherosclerosis based on interpretable machine models in a Chinese T2DM population.

Background: Cardiovascular disease (CVD) has emerged as a global public health concern. Identifying and preventing subclinical atherosclerosis (SCAS), an early indicator of CVD, is critical for improving cardiovascular outcomes. This study aimed to construct interpretable machine learning models for predicting SCAS risk in type 2 diabetes mellitus (T2DM) patients. Methods: This study included 3084 T2DM individuals who received health care at Zhenhai Lianhua Hospital, Ningbo, China, from January 2018 to December 2022. The least absolute shrinkage and selection operator combined with random forest-recursive feature elimination were used to screen for characteristic variables. Linear discriminant analysis, logistic regression, Naive Bayes, random forest, support vector machine, and extreme gradient boosting were employed in constructing risk prediction models for SCAS in T2DM patients. The area under the receiver operating characteristic curve (AUC) was employed to assess the predictive capacity of the model through 10-fold cross-validation. Additionally, the SHapley Additive exPlanations were utilized to interpret the best-performing model. Results: The percentage of SCAS was 38.46% (n=1186) in the study population. Fourteen variables, including age, white blood cell count, and basophil count, were identified as independent risk factors for SCAS. Nine predictors, including age, albumin, and total protein, were screened for the construction of risk prediction models. After validation, the random forest model exhibited the best clinical predictive value in the training set with an AUC of 0.729 (95% CI: 0.709-0.749), and it also demonstrated good predictive value in the internal validation set [AUC: 0.715 (95% CI: 0.688-0.742)]. The model interpretation revealed that age, albumin, total protein, total cholesterol, and serum creatinine were the top five variables contributing to the prediction model. Conclusion: The construction of SCAS risk models based on the Chinese T2DM population contributes to its early prevention and intervention, which would reduce the incidence of adverse cardiovascular prognostic events.

High free triiodothyronine, and free-triiodothyronine-to-free-thyroxine ratio are associated with metabolic syndrome in a euthyroid employee population: the Zhejiang Zhenhai study.

Objective: The aim of this study was to elaborate the link of thyroid hormones (THs) and metabolic syndrome (MetS) in a Chinese euthyroid employee population with MetS component(s). Methods: An annual health checkup was performed on employees in 2019. Anthropometric parameters, metabolic parameters, and thyroid function were measured. A questionnaire was used in conjunction with Zhenhai Lianhua Hospital database to receive employees' medication records and thyroid surgical history records. Results: A total of 5486 eligible employees were included; the prevalence of MetS was generally higher in males than in females (38.9 vs. 30.4%, P < 0.001). Among employees with central obesity, hypertriglyceridemia, hyperglycemia, hypertension, and low high-density lipoprotein cholesterol (HDL-C), the prevalence of MetS was 68.8, 63.6, 68.2, 48.8, and 60.0% in males and 72.6, 63.3, 61.3, 42.3, and 42.3% in females, respectively. Logistic regression analysis showed that thyroid-stimulating hormone and free thyroxine (FT4) quartiles had no significant impact on MetS. Free triiodothyronine/free thyroxine (FT3/FT4) and free triiodothyronine (FT3)) quartiles were positively associated with the increased odds ratio (OR) for MetS and dyslipidemia (hypertriglyceridemia and low HDL-C), regardless of gender. In males, FT3 and FT3/FT4 quartiles were positively associated with the OR for central obesity, whereas FT4 quartiles were negatively associated; both FT3 and FT4 quartiles were positively associated with increased OR of hyperglycemia, while similar results were not observed in females. Interaction analysis indicated no significant effect of gender and TH interactions on risk of MetS. Conclusion: High FT3 and FT3/FT4 were strongly linked with MetS and dyslipidemia in our study, even in the euthyroid individuals. Tighter control of thyroid function was necessary for those with preexisting MetS component(s).

Cobalt protoporphyrin-induced nano-self-assembly for CT imaging, magnetic-guidance, and antioxidative protection of stem cells in pulmonary fibrosis treatment.

Mesenchymal stem cells (MSCs) transplantation is a promising approach for pulmonary fibrosis (PF), however it is impeded by several persistent challenges, including the lack of long-term tracking, low retention, and poor survival of MSCs, as well as the low labeling efficiency of nanoprobes. Herein, a cobalt protoporphyrin IX (CoPP) aggregation-induced strategy is applied to develop a multifunctional nano-self-assembly (ASCP) by combining gold nanoparticle (AuNPs), superparamagnetic iron oxide nanoparticles (SPIONs), and CoPP through a facile solvent evaporation-driven approach. Since no additional carrier materials are employed during the synthesis, high loading efficiency of active ingredients and excellent biocompatibility are achieved. Additionally, facile modification of the ASCPs with bicyclo[6.1.0]nonyne (BCN) groups (named as ASCP-BCN) enables them to effectively label MSCs through bioorthogonal chemistry. The obtained ASCP-BCN could not only help to track MSCs with AuNP-based computed tomography (CT) imaging, but also achieve an SPIONs-assisted magnetic field based improvement in the MSCs retention in lungs as well as promoted the survival of MSCs via the sustained release of CoPP. The in vivo results demonstrated that the labeled MSCs improved the lung functions and alleviated the fibrosis symptoms in a bleomycin-induced PF mouse model. Collectively, a novel ASCP-BCN multifunctional nanoagent was developed to bioorthogonally-label MSCs with a high efficiency, presenting a promising potential in the high-efficient MSC therapy for PF.

The Metabolic Score for Insulin Resistance Index is Superior to the Triglyceride and Glucose Index in Identifying Nonalcoholic Fatty Liver Disease in Hyperuricemia Subjects.

Purpose: Hyperuricemia (HUA) is a recognized risk factor for non-alcoholic fatty liver disease (NAFLD). Early diagnosis and the identification of predictive factors are important for the HUA population. This study aimed to investigate the potential relationship between the metabolic score for insulin resistance (METS-IR) index and the triglyceride and glucose (TyG) index with NAFLD in individuals with HUA. Patients and Methods: A total of 5537 HUA participants were included in the study. Hepatic ultrasonography was conducted following the latest diagnostic criteria to diagnose NAFLD. The relationship between the TyG and METS-IR index and NAFLD was evaluated by multivariable logistic regression analysis and restricted cubic spline model (RCS). In addition, the area under the curve (AUC), positive likelihood ratio, and negative likelihood ratio were explored to compare the predictive value of IR surrogates for NAFLD with HUA. The AUCs of the two indicators were compared using the DeLong test. Results: Multivariable logistic regression analysis revealed that elevated TyG (OR = 2.285, 95% CI: 1.525, 3.428) and METS-IR (OR = 1.242, 95% CI: 1.219, 1.266) indices significantly increase the risk of NAFLD. Meanwhile, the RCS analysis revealed a -log-shaped nonlinear relationship between NAFLD risk and the METS-IR index (P non-linear < 0.001), contrasting with the linear association observed with the TyG index (P non-linear = 0.763). Notably, the risk of NAFLD demonstrated a significant escalation when the METS-IR index exceeded the threshold of 39.208 (OR=1). Compared to the TyG index (AUC = 0.734, 95% CI: 0.721~0.748), the METS-IR index (AUC = 0.821, 95% CI: 0.810~0.832) demonstrated superior predictive value for NAFLD in individuals with HUA according to the DeLong test. Conclusion: In the HUA population, the METS-IR index has a higher predictive value for NAFLD than the TyG index, contributing to early diagnosis and disease prevention.

Better future with better us: Exploring young people's energy-saving behavior based on norm activation theory.

Conserving energy use is a shared responsibility of all people, and it is essential for mitigating climate warming. The purpose of this study is to investigate energy-saving behaviors and the influencing factors of young people. We developed a new theoretical framework by adding self-efficacy and information publicity to norm activation theory. Partial least squares structural equation modeling was used to analyze 360 sample data from different regions in China. The findings show that attribution of responsibility and awareness of consequence are important prerequisites for personal norm. Personal norm positively influences energy-saving intention. Interestingly, information publicity has a significant positive effect on both intention and behavior to save energy, while self-efficacy only significantly affects energy-saving intention. This study focuses on the young group and enriches the research on factors influencing residents' energy-saving behaviors. The findings provide insightful ideas for governments and communities to guide individual energy conservation behaviors.